1. Field of the Invention
The present invention relates to a learning control apparatus for a reversing rolling mill that rolls strip, etc. using the reversing method.
2. Description of the Related Art
In a rolling mill that rolls strip, etc. using the reversing method, highly accurate maintenance of the set up values in each pass is essential for stable operation, and set up calculation systems and learning controls that use various mathematical models are widely employed for that purpose. In the case of rolling using the reversing method, the set up calculation is generally executed by a process computer; an optimum pass schedule calculation for the strip delivery thickness for each pass is carried out for processing optimisation, and the various set up values required in the operation of the mill for each pass are calculated. For example, such settings as the roll gap for each pass, the strip threading speed, the running speed, the tail-out speed, and the side guides and roll coolant flow are calculated as these setting values, and these data are transmitted to lower level controllers.
In order to achieve stable operation at such a time, highly accurate prediction of the various quantities by mathematical models is a basic requirement. However, actual rolling operations are affected by all kinds of disturbance
factors due to intermittent conditions. Thus, however carefully constructed a mathematical model may be, it will not be possible completely to grasp the actual conditions and numerically to express them.
Therefore, in order to reflect in model prediction various disturbance factors such as disturbances that vary in a time series fashion, learning control is generally applied to the mathematical models. With relation to learning control for reversing rolling mills, various methods have been proposed up to the present time, and each has had some effect.
For example, a xe2x80x9cPlate Rolling Methodxe2x80x9d that rolls while adding corrections so that the settings will be optimum for the next pass by measuring the actual values at each pass midway through the rolling pass and making learning
calculations using those results has been disclosed in Laid-Open Patent No. Heisei 7-60320 Gazette. In the method stated in this Gazette, since rolling is continued while measuring actual values midway through a rolling pass and sequentially adding corrections so that the set up for the next pass are optimum, the measurement results of the actual values for each pass in rolling the relevant strip can be reflected in the calculation of the setting values for the next pass. However, there is the problem that errors that are not sequentially present in a pass in time series fashion cannot be reflected in the rolling of the next strip.
Also, a xe2x80x9cA Reversing Type Rolling Method That Excels In Configuration And Strip Thickness Controlxe2x80x9d that, for the second pass and thereafter, performs rolling while repeating the learning, during or immediately after the rolling of each pass, of corrections to the pass schedule up to the final pass based on load forecast expressions learned from the previous pass, in almost the same way as in the above Gazette, has been disclosed in Laid-Open Patent No. Heisei 8-243614 Gazette.
With this Gazette also, learning calculations can be performed based on the calculation results of the actual values for each pass and these results can be reflected in the settings for the next and subsequent passes. However, there is the problem that, even though they may be time series-wise, errors that do not continue in every pass cannot always appropriately be taken into consideration.
At the same time, methods are disclosed in Laid-Open Patent No. Heisei 2-137606 Gazette and Laid-Open Patent No. Heisei 4-367901 Gazette that assimilate model errors that depend on the material being rolled or processed, its target dimensions, etc. out of changes of state that are not time series dependent by storing them in tables prepared group by group. However, even though intrinsic model errors that depend on the material and target dimensions can be assimilated by these methods, this is nothing more than the assimilation of errors as statistical results, and there is the problem that time series errors that do not sequentially depend on passes cannot be assimilated.
As explained above, with the methods disclosed in the above several Gazettes, it is possible effectively to learn model errors that arise sequentially in each pass and intrinsic model errors dependent on materials, target dimensions, etc. when using each method, and there may be cases when product qualities such as strip thickness, strip crown and flatness are well maintained. However, there was the problem of how to assimilate factors other than the above-mentioned model errors, that is to say errors that, though they are time series type errors, are not sequentially dependent in each pass, in order further to maintain stable model forecast accuracy and improve product quality.
The reasons why a satisfactory product quality cannot be achieved solely by assimilation of model errors arising sequentially in each pass and intrinsic model errors depending on the material and the target dimensions using learning calculations for each individual error, as mentioned above, are as follows.
That is to say, a pass schedule in a reversing rolling mill composed of 1xcx9cN passes, taking N as an integer of 2 or more, is generally made up of several parts such as initial stage passesxe2x80x94intermediate passesxe2x80x94final stage passes or rough passesxe2x80x94finishing passes, and the essential points for the operations in the passes pertinent to each part differ. For example, with the initial stage passes, the pass schedule is determined so that the roll force is increased for a few passes in order to improve productivity while, conversely, with the final stage passes, it is normal to correct the pass schedule in order to satisfy different aims from those of the initial stage passes, such as ensuring the surface quality of the product.
Consequently, for example, operating conditions such as the reduction (percent draft) rate limits for each pass differ in each part and, naturally, the behavior of the model errors will differ according to those parameter limits. Therefore this means that, with learning calculations that are performed at each relevant pass, the problem will remain that it is not possible satisfactorily to assimilate time series model errors. In fact, since variations with passage of time, such as roll surface state, appear as behavior such as the coefficient of friction gradually varying in time series fashion with the progress of a pass, a method that reflects in the model calculation for the next pass the results of learning by measurement of actual values in a pass will be effective. On the other hand, since the behavior of model errors for material deformation resistance and the like is not simple, cases will often be observed in which the results of time series-wise learning calculations using the actual values of the previous pass do not necessarily operate toward assimilation of the model errors of the next pass.
Also, while on the one hand it is possible, by taking some amount of time, to assimilate the medium-term and long-term fluctuations of model errors by the method of storing the results of learning calculations in tables divided into groups for every material and product dimension, it is not possible to assimilate the minute fluctuations of model errors that occur in successive products in the operations of one day. That is to say, in reversing rolling, rolling is based on pre-stored pass schedules or based on optimum pass schedules that are generated by logic. If, at that time, there are not sufficiently many opportunities for renewing one by one the learning calculation values that belong to the same group division, and also if group division tables that are subdivided to that extent are not prepared, there will, conversely, be many cases when the fluctuations of model errors in the medium term or long term cannot be stabilized and assimilated.
The above type of problems occur because, with a method that sequentially assimilates the model errors of the next pass based on the actual values of the previous pass, the pass schedule composition for a reversing rolling mill is not always uniform. Consequently, neither can model error behavior be completely assimilated by just applying the learning calculation values based on the actual values for the previous pass to the model calculations for the next pass.
On the other hand, with the method of assimilating the intrinsic model errors in the medium term or the long term, for example, fluctuations of model errors such as occur during one day""s operations cannot be assimilated sufficiently rapidly. Also, in particular, the fact that there are many cases when, due to the pass schedule composition for a reversing rolling mill not always being uniform, the method of performing learning calculations based on the actual values in the previous pass and sequentially assimilating the model errors of the next pass does not work effectively is a great inherent problem, with its possibility of adversely affecting stable model prediction and, in turn, leading to instability in operations as a whole.
Accordingly, one object of the present invention is to provide a novel learning control apparatus in a reversing rolling mill in order to solve such problems. The learning control apparatus of the present invention adds a learning calculation means that performs learning calculations based
on actual values in the previous pass so as to be able to cope with the pass schedule composition peculiar to reversing rolling mills, sequentially assimilates model errors of the next pass and, at the same time, after completion of rolling of the rolling strip currently being rolled (hereafter also called xe2x80x9cthe strip concernedxe2x80x9d), is able to reflect the learning calculation values in the i passes of the previously rolled rolling strip (hereafter also called xe2x80x9cthe previous stripxe2x80x9d) in the model calculations in the i passes of the rolling strip next to be rolled (hereafter also called xe2x80x9cthe next stripxe2x80x9d). Furthermore, by storing learning calculation values of medium-term or long-term model errors that depend on materials and product dimensions in tables prepared group division by group division that can assimilate intrinsic model errors, the learning control apparatus of the present invention makes possible the performance of stable operation and, moreover, can also improve the quality accuracy of the product.
In order to achieve the above object, the present invention has the following composition. That is to say, in a learning control apparatus for a reversing rolling mill that, when controlling a reversing rolling mill in performing the rolling of N passes, taking N as an integer of 2 or more, computes the required setting values for the mill operation of each pass by performing an optimum pass schedule calculation using a mathematical model and, at the same time applying learning control,
the present invention has the characteristic of providing:
N in number actual data gathering means that, taking i as any pass out of 1xcx9cN passes, collect the actual values obtained in pass i rolling after the current material has been respectively rolled in the i passes, find actual-calculated values by executing model calculations in every pass i using these actual values, and store these actual values and actual-calculated values;
(Nxe2x88x921) in number pass to pass learning calculation means that include:
a pass 1 pass to pass learning calculation means that, from among the actual data gathering means, computes a learning term that assimilates a pass to pass 1 model error based on the actual value and the actual-calculated value stored in the pass 1 actual data gathering means, and stores this learning term, and
pass i pass to pass learning calculation means that, taking any pass from among 2xcx9c(Nxe2x88x921) passes as pass i, compute learning terms that assimilate the model errors of sequential pass to passes i based on the actual values and actual-calculated values that are stored in the respective pass i actual data gathering means together with the learning term for pass 1 that is stored in the pass 1 pass to pass learning calculation means, and store these learning terms;
(Nxe2x88x921) in number next pass set up calculation means that sequentially compute the required setting values for the operation of the mill for 2xcx9cN passes based on the learning terms respectively stored in the (Nxe2x88x921) in number pass to pass learning calculation means; N in number direct pass to pass learning calculation means that compute learning terms that assimilate the errors for the next material from pass 1 to pass N based on the actual values and actual-calculated values for pass 1 to pass N of the current material that are respectively stored in the N in number actual data gathering means;
a lot to lot learning calculation means that, based on the model actual values and actual-calculated value s for each pass i from pass 1 to pass N of the current material that are respectively stored in the N in number actual data gathering means, educes the model error parts that depend on the material and product dimensions, finds a learning term that assimilates the lot to lot model errors, and stores this learning term in a table, and
N in number next material pass setting means that, taking N as an integer of 2 or more, and based on the learning terms that are stored in the direct pass to pass learning calculation means and the learning term stored in the lot to lot learning calculation means, sequentially computes the required setting values for the operation of the mill for each pass from pass 1 to the final pass for the next material,
and after completion of the rolling of pass 1 of the current material, of executing the rolling of pass 2 onwards in conformity with the setting values of the next pass set up calculation means and executing the rolling of pass 1 to pass N for the next material in conformity with the setting values of the next material pass set up means.